{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/admin/Dropbox/TAMU/Diss./Theory/Choice_set/RnR/Dataverse/FPA_Appx.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 1 Jan 2021, 23:46:14
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. ***     Data prep       *********************
. 
. ///     Create dependent variable: total options in choice-set
> ///     Since each policy is mesaured with a binary indicator, I add all 7 policy options
> ///     and create a variable for the total number of policies in a respondent's choice-set.
>  
. gen set_all=p1+p2+p3+p4+p5+p6+p7
{txt}
{com}. 
. /// Create variables for treatments in reduced sample (no control conditions)
> gen cas=.
{txt}(1036 missing values generated)

{com}. replace cas=0 if casualties==1
{txt}(425 real changes made)

{com}. replace cas=1 if casualties==2
{txt}(398 real changes made)

{com}. 
. gen oth=.
{txt}(1036 missing values generated)

{com}. replace oth=0 if other==1
{txt}(416 real changes made)

{com}. replace oth=1 if other==2
{txt}(407 real changes made)

{com}. 
. ********************************
. *********       Analyses        ********
. ********************************
. 
. *** Appendix B: Internal validity tests
. /// Factual Manipulation checks
> 
. tab fmc1

                        {txt}FMC1 {c |}      Freq.     Percent        Cum.
{hline 29}{c +}{hline 35}
An American embassy building {c |}{res}         89        8.59        8.59
{txt}   An American military base {c |}{res}        947       91.41      100.00
{txt}{hline 29}{c +}{hline 35}
                       Total {c |}{res}      1,036      100.00
{txt}
{com}. tab fmc2

       {txt}FMC2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
Afghanistan {c |}{res}        932       89.96       89.96
{txt}       Iran {c |}{res}         83        8.01       97.97
{txt}      Kenya {c |}{res}         21        2.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,036      100.00
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Time horizons manipulation check (0=ST; 1=LT)
> tab tf_mc if horizon==0

      {txt}TF_MC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        436       84.82       84.82
{txt}          1 {c |}{res}         78       15.18      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        514      100.00
{txt}
{com}. tab tf_mc if horizon==1

      {txt}TF_MC {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        114       21.84       21.84
{txt}          1 {c |}{res}        408       78.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        522      100.00
{txt}
{com}. anova tf_mc horizon casualties other

                           {txt}Number of obs ={res}    1036     {txt}R-squared     ={res}  0.4005
                           {txt}Root MSE      ={res} .387346     {txt}Adj R-squared ={res}  0.3981

                  {txt}Source {c |}  Partial SS    df       MS           F     Prob > F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 103.323848     4  25.8309621     172.16     0.0000
                         {txt}{c |}
                 horizon {c |} {res} 103.070599     1  103.070599     686.97     0.0000
{txt}              casualties {c |} {res} .334071999     2     .167036       1.11     0.3289
{txt}                   other {c |} {res} .459406591     1  .459406591       3.06     0.0804
                         {txt}{c |}
                Residual {c |} {res} 154.687735  1031    .1500366   
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 258.011583  1035  .249286554   
{txt}
{com}. tukeyhsd horizon

{txt}Tukey HSD pairwise comparisons for variable horizon
studentized range critical value(.05, 2, 1031) = 2.7750654
uses harmonic mean sample size =  517.969

                                       mean 
grp vs grp       group means           dif    HSD-test
-------------------------------------------------------
{res}  1 vs   2     0.1518     0.7816      0.6299  37.0080{txt}*

Note: the levels of horizon have been recoded.

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Costs and Importance
> anova costs horizon casualties other

                           {txt}Number of obs ={res}    1036     {txt}R-squared     ={res}  0.0058
                           {txt}Root MSE      ={res} 1.18042     {txt}Adj R-squared ={res}  0.0020

                  {txt}Source {c |}  Partial SS    df       MS           F     Prob > F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 8.42032602     4   2.1050815       1.51     0.1968
                         {txt}{c |}
                 horizon {c |} {res} 1.64479513     1  1.64479513       1.18     0.2775
{txt}              casualties {c |} {res} 6.76514788     2  3.38257394       2.43     0.0888
{txt}                   other {c |} {res} .051475432     1  .051475432       0.04     0.8476
                         {txt}{c |}
                Residual {c |} {res} 1436.57871  1031  1.39338381   
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 1444.99903  1035  1.39613433   
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. sum important, detail

                          {txt}Important
{hline 61}
      Percentiles      Smallest
 1%    {res}        2              1
{txt} 5%    {res}        3              1
{txt}10%    {res}        4              1       {txt}Obs         {res}       1036
{txt}25%    {res}        5              1       {txt}Sum of Wgt. {res}       1036

{txt}50%    {res}        6                      {txt}Mean          {res} 5.739382
                        {txt}Largest       Std. Dev.     {res} 1.315692
{txt}75%    {res}        7              7
{txt}90%    {res}        7              7       {txt}Variance      {res} 1.731047
{txt}95%    {res}        7              7       {txt}Skewness      {res}-1.172644
{txt}99%    {res}        7              7       {txt}Kurtosis      {res}  4.27538
{txt}
{com}. reg important horizon casualties other

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}    1036
{txt}{hline 13}{char +}{hline 30}           F(  3,  1032) ={res}    1.91
    {txt}   Model {char |} {res} 9.88470335     3  3.29490112           {txt}Prob > F      = {res} 0.1265
    {txt}Residual {char |} {res}  1781.7485  1032  1.72650049           {txt}R-squared     = {res} 0.0055
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.0026
    {txt}   Total {char |} {res}  1791.6332  1035  1.73104657           {txt}Root MSE      = {res}  1.314

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}   important{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}horizon {c |}{col 14}{res}{space 2}-.1757327{col 26}{space 2}  .081673{col 37}{space 1}   -2.15{col 46}{space 3}0.032{col 54}{space 4}-.3359967{col 67}{space 3}-.0154686
{txt}{space 2}casualties {c |}{col 14}{res}{space 2} .0681092{col 26}{space 2} .0719085{col 37}{space 1}    0.95{col 46}{space 3}0.344{col 54}{space 4}-.0729944{col 67}{space 3} .2092128
{txt}{space 7}other {c |}{col 14}{res}{space 2}-.0742329{col 26}{space 2} .0715667{col 37}{space 1}   -1.04{col 46}{space 3}0.300{col 54}{space 4}-.2146658{col 67}{space 3} .0661999
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.835789{col 26}{space 2} .0919386{col 37}{space 1}   63.47{col 46}{space 3}0.000{col 54}{space 4} 5.655381{col 67}{space 3} 6.016197
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. margins, at(horizon=(0 1))
{res}
{txt}Predictive margins{col 51}Number of obs{col 67}= {res}      1036
{txt}Model VCE{col 14}: {res}OLS

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:horizon}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 5.827927{col 26}{space 2} .0579653{col 37}{space 1}  100.54{col 46}{space 3}0.000{col 54}{space 4} 5.714184{col 67}{space 3}  5.94167
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 5.652194{col 26}{space 2} .0575193{col 37}{space 1}   98.27{col 46}{space 3}0.000{col 54}{space 4} 5.539326{col 67}{space 3} 5.765063
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. *** Appendix C: Choice-set size
. /// OLS regression models
> /// Models 1&2 below replicate the analysis in the main text,
> /// and fit the marginal effects plots in figure 5 in main text.
> /// Model 3 replicate the analysis of the reduced sample,
> /// results fir with the density plot in figure 4.
> 
. /// Model 1: Experimental treatments only 
> reg set_all horizon i.casualties other 

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}    1036
{txt}{hline 13}{char +}{hline 30}           F(  4,  1031) ={res}   15.61
    {txt}   Model {char |} {res} 136.580215     4  34.1450537           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 2254.84836  1031  2.18704981           {txt}R-squared     = {res} 0.0571
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.0535
    {txt}   Total {char |} {res} 2391.42857  1035  2.31055901           {txt}Root MSE      = {res} 1.4789

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     set_all{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}horizon {c |}{col 14}{res}{space 2} .1543136{col 26}{space 2} .0919405{col 37}{space 1}    1.68{col 46}{space 3}0.094{col 54}{space 4}-.0260982{col 67}{space 3} .3347254
{txt}{space 12} {c |}
{space 2}casualties {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-1.043929{col 26}{space 2} .1975444{col 37}{space 1}   -5.28{col 46}{space 3}0.000{col 54}{space 4}-1.431564{col 67}{space 3}-.6562935
{txt}{space 10}2  {c |}{col 14}{res}{space 2} -1.03835{col 26}{space 2} .1992766{col 37}{space 1}   -5.21{col 46}{space 3}0.000{col 54}{space 4}-1.429384{col 67}{space 3}-.6473158
{txt}{space 12} {c |}
{space 7}other {c |}{col 14}{res}{space 2} .1146424{col 26}{space 2} .1031628{col 37}{space 1}    1.11{col 46}{space 3}0.267{col 54}{space 4}-.0877907{col 67}{space 3} .3170754
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.899007{col 26}{space 2} .1113597{col 37}{space 1}   35.01{col 46}{space 3}0.000{col 54}{space 4} 3.680489{col 67}{space 3} 4.117524
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Model 2: Full model
> reg set_all horizon other i.casualties gender age party fp_know edu_cat 

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}    1034
{txt}{hline 13}{char +}{hline 30}           F(  9,  1024) ={res}   14.34
    {txt}   Model {char |} {res} 265.949616     9  29.5499573           {txt}Prob > F      = {res} 0.0000
    {txt}Residual {char |} {res} 2110.02427  1024  2.06057058           {txt}R-squared     = {res} 0.1119
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.1041
    {txt}   Total {char |} {res} 2375.97389  1033  2.30007153           {txt}Root MSE      = {res} 1.4355

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     set_all{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}horizon {c |}{col 14}{res}{space 2} .1473224{col 26}{space 2}  .089806{col 37}{space 1}    1.64{col 46}{space 3}0.101{col 54}{space 4}-.0289023{col 67}{space 3} .3235472
{txt}{space 7}other {c |}{col 14}{res}{space 2}  .145943{col 26}{space 2} .1004654{col 37}{space 1}    1.45{col 46}{space 3}0.147{col 54}{space 4}-.0511986{col 67}{space 3} .3430846
{txt}{space 12} {c |}
{space 2}casualties {c |}
{space 10}1  {c |}{col 14}{res}{space 2}-1.021852{col 26}{space 2} .1920604{col 37}{space 1}   -5.32{col 46}{space 3}0.000{col 54}{space 4}-1.398729{col 67}{space 3}-.6449753
{txt}{space 10}2  {c |}{col 14}{res}{space 2}-1.041705{col 26}{space 2} .1936608{col 37}{space 1}   -5.38{col 46}{space 3}0.000{col 54}{space 4}-1.421722{col 67}{space 3}-.6616874
{txt}{space 12} {c |}
{space 6}gender {c |}{col 14}{res}{space 2} .0345376{col 26}{space 2}  .092009{col 37}{space 1}    0.38{col 46}{space 3}0.707{col 54}{space 4}-.1460101{col 67}{space 3} .2150852
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0127521{col 26}{space 2} .0036053{col 37}{space 1}   -3.54{col 46}{space 3}0.000{col 54}{space 4}-.0198267{col 67}{space 3}-.0056774
{txt}{space 7}party {c |}{col 14}{res}{space 2} .1054883{col 26}{space 2} .0225218{col 37}{space 1}    4.68{col 46}{space 3}0.000{col 54}{space 4} .0612942{col 67}{space 3} .1496823
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .2191124{col 26}{space 2} .0558674{col 37}{space 1}    3.92{col 46}{space 3}0.000{col 54}{space 4} .1094848{col 67}{space 3}   .32874
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .1095289{col 26}{space 2}  .054018{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} .0035302{col 67}{space 3} .2155275
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 3.002145{col 26}{space 2} .2969483{col 37}{space 1}   10.11{col 46}{space 3}0.000{col 54}{space 4} 2.419448{col 67}{space 3} 3.584841
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Model 3: Reduced sample (also in main text .do file)
> /// Create varibles for control conditions only (reduced sample analysis)
> gen horz=.
{txt}(1036 missing values generated)

{com}. replace horz=horizon if casualties==0
{txt}(213 real changes made)

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. /// Create varible for control conditions and choice-set size 
> gen horz2=horz if set_all>1
{txt}(834 missing values generated)

{com}. 
. /// Run univariate OLS regression model with reduced sample
> /// Compute mean values for both conditions using marginal effects (fit to figure 4 in main text)
> /// Model results (table 3, model 3)
> reg set_all horz2 gender party age fp_know edu_cat 

      {txt}Source {c |}       SS       df       MS              Number of obs ={res}     202
{txt}{hline 13}{char +}{hline 30}           F(  6,   195) ={res}    2.22
    {txt}   Model {char |} {res} 27.4182806     6  4.56971344           {txt}Prob > F      = {res} 0.0432
    {txt}Residual {char |} {res} 402.126274   195  2.06218602           {txt}R-squared     = {res} 0.0638
{txt}{hline 13}{char +}{hline 30}           Adj R-squared = {res} 0.0350
    {txt}   Total {char |} {res} 429.544554   201  2.13703758           {txt}Root MSE      = {res}  1.436

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     set_all{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}horz2 {c |}{col 14}{res}{space 2} .3419019{col 26}{space 2} .2045569{col 37}{space 1}    1.67{col 46}{space 3}0.096{col 54}{space 4}-.0615261{col 67}{space 3} .7453298
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .1378316{col 26}{space 2} .2053874{col 37}{space 1}    0.67{col 46}{space 3}0.503{col 54}{space 4}-.2672344{col 67}{space 3} .5428975
{txt}{space 7}party {c |}{col 14}{res}{space 2} .0854974{col 26}{space 2} .0492709{col 37}{space 1}    1.74{col 46}{space 3}0.084{col 54}{space 4} -.011675{col 67}{space 3} .1826698
{txt}{space 9}age {c |}{col 14}{res}{space 2} -.008348{col 26}{space 2} .0082652{col 37}{space 1}   -1.01{col 46}{space 3}0.314{col 54}{space 4}-.0246487{col 67}{space 3} .0079526
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .2597339{col 26}{space 2} .1188311{col 37}{space 1}    2.19{col 46}{space 3}0.030{col 54}{space 4} .0253748{col 67}{space 3} .4940931
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .0149653{col 26}{space 2} .1159734{col 37}{space 1}    0.13{col 46}{space 3}0.897{col 54}{space 4} -.213758{col 67}{space 3} .2436886
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}  3.18633{col 26}{space 2} .6287473{col 37}{space 1}    5.07{col 46}{space 3}0.000{col 54}{space 4} 1.946312{col 67}{space 3} 4.426348
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. margins, at(horz2=(0 1))
{res}
{txt}Predictive margins{col 51}Number of obs{col 67}= {res}       202
{txt}Model VCE{col 14}: {res}OLS

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Linear prediction, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:horz2}{space 11}{txt:=} {space 10}0}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:horz2}{space 11}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 3.977564{col 26}{space 2} .1437697{col 37}{space 1}   27.67{col 46}{space 3}0.000{col 54}{space 4} 3.694021{col 67}{space 3} 4.261107
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 4.319466{col 26}{space 2} .1437697{col 37}{space 1}   30.04{col 46}{space 3}0.000{col 54}{space 4} 4.035923{col 67}{space 3} 4.603009
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. *** Appendix C: Choice-set composition
. /// Probit interaction models (reduced sample, no control conditions)
> /// Results of these models are displayed in appendix file (table 3, models 1&2)
> probit p1 i.horizon i.oth i.cas i.horizon##i.oth gender age party fp_know edu_cat

{res}{txt}Iteration 0:{space 3}log likelihood = {res: -480.1729}  
Iteration 1:{space 3}log likelihood = {res: -342.2914}  
Iteration 2:{space 3}log likelihood = {res:-337.90064}  
Iteration 3:{space 3}log likelihood = {res:-337.88262}  
Iteration 4:{space 3}log likelihood = {res:-337.88262}  
{res}
{txt}Probit regression{col 51}Number of obs{col 67}= {res}       821
{txt}{col 51}LR chi2({res}9{txt}){col 67}= {res}    284.58
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-337.88262{txt}{col 51}Pseudo R2{col 67}= {res}    0.2963

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          p1{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.horizon {c |}{col 14}{res}{space 2} .1232022{col 26}{space 2} .2225798{col 37}{space 1}    0.55{col 46}{space 3}0.580{col 54}{space 4}-.3130461{col 67}{space 3} .5594505
{txt}{space 7}1.oth {c |}{col 14}{res}{space 2}-1.977937{col 26}{space 2}     .177{col 37}{space 1}  -11.17{col 46}{space 3}0.000{col 54}{space 4} -2.32485{col 67}{space 3}-1.631023
{txt}{space 7}1.cas {c |}{col 14}{res}{space 2} -.096351{col 26}{space 2} .1105881{col 37}{space 1}   -0.87{col 46}{space 3}0.384{col 54}{space 4}-.3130996{col 67}{space 3} .1203977
{txt}{space 12} {c |}
{space 1}horizon#oth {c |}
{space 8}1 1  {c |}{col 14}{res}{space 2} .4292367{col 26}{space 2} .2564686{col 37}{space 1}    1.67{col 46}{space 3}0.094{col 54}{space 4}-.0734326{col 67}{space 3}  .931906
{txt}{space 12} {c |}
{space 6}gender {c |}{col 14}{res}{space 2} .1152895{col 26}{space 2} .1152772{col 37}{space 1}    1.00{col 46}{space 3}0.317{col 54}{space 4}-.1106497{col 67}{space 3} .3412288
{txt}{space 9}age {c |}{col 14}{res}{space 2} .0073238{col 26}{space 2}  .004559{col 37}{space 1}    1.61{col 46}{space 3}0.108{col 54}{space 4}-.0016117{col 67}{space 3} .0162593
{txt}{space 7}party {c |}{col 14}{res}{space 2} .0867894{col 26}{space 2} .0279931{col 37}{space 1}    3.10{col 46}{space 3}0.002{col 54}{space 4} .0319239{col 67}{space 3}  .141655
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}  -.05549{col 26}{space 2} .0710893{col 37}{space 1}   -0.78{col 46}{space 3}0.435{col 54}{space 4}-.1948224{col 67}{space 3} .0838425
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .0441662{col 26}{space 2}  .068398{col 37}{space 1}    0.65{col 46}{space 3}0.518{col 54}{space 4}-.0898914{col 67}{space 3} .1782239
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.059225{col 26}{space 2} .3737589{col 37}{space 1}    2.83{col 46}{space 3}0.005{col 54}{space 4} .3266713{col 67}{space 3} 1.791779
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. probit p2 i.horizon i.oth i.cas i.horizon##i.oth gender age party fp_know edu_cat

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-517.83484}  
Iteration 1:{space 3}log likelihood = {res:-411.41128}  
Iteration 2:{space 3}log likelihood = {res:-410.14109}  
Iteration 3:{space 3}log likelihood = {res:-410.13996}  
Iteration 4:{space 3}log likelihood = {res:-410.13996}  
{res}
{txt}Probit regression{col 51}Number of obs{col 67}= {res}       821
{txt}{col 51}LR chi2({res}9{txt}){col 67}= {res}    215.39
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-410.13996{txt}{col 51}Pseudo R2{col 67}= {res}    0.2080

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          p2{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}1.horizon {c |}{col 14}{res}{space 2} .2278596{col 26}{space 2} .1251036{col 37}{space 1}    1.82{col 46}{space 3}0.069{col 54}{space 4}-.0173391{col 67}{space 3} .4730582
{txt}{space 7}1.oth {c |}{col 14}{res}{space 2}  1.48123{col 26}{space 2} .1480354{col 37}{space 1}   10.01{col 46}{space 3}0.000{col 54}{space 4} 1.191086{col 67}{space 3} 1.771374
{txt}{space 7}1.cas {c |}{col 14}{res}{space 2}-.1861998{col 26}{space 2} .1010392{col 37}{space 1}   -1.84{col 46}{space 3}0.065{col 54}{space 4}-.3842329{col 67}{space 3} .0118333
{txt}{space 12} {c |}
{space 1}horizon#oth {c |}
{space 8}1 1  {c |}{col 14}{res}{space 2}-.0777677{col 26}{space 2} .2140127{col 37}{space 1}   -0.36{col 46}{space 3}0.716{col 54}{space 4}-.4972249{col 67}{space 3} .3416895
{txt}{space 12} {c |}
{space 6}gender {c |}{col 14}{res}{space 2}-.0509545{col 26}{space 2} .1038024{col 37}{space 1}   -0.49{col 46}{space 3}0.624{col 54}{space 4}-.2544034{col 67}{space 3} .1524945
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0024747{col 26}{space 2} .0040584{col 37}{space 1}   -0.61{col 46}{space 3}0.542{col 54}{space 4}-.0104289{col 67}{space 3} .0054795
{txt}{space 7}party {c |}{col 14}{res}{space 2}-.0333568{col 26}{space 2} .0257781{col 37}{space 1}   -1.29{col 46}{space 3}0.196{col 54}{space 4}-.0838809{col 67}{space 3} .0171674
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}-.0214695{col 26}{space 2} .0636583{col 37}{space 1}   -0.34{col 46}{space 3}0.736{col 54}{space 4}-.1462375{col 67}{space 3} .1032986
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .1229644{col 26}{space 2} .0613047{col 37}{space 1}    2.01{col 46}{space 3}0.045{col 54}{space 4} .0028094{col 67}{space 3} .2431195
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.3063438{col 26}{space 2} .3123112{col 37}{space 1}   -0.98{col 46}{space 3}0.327{col 54}{space 4}-.9184626{col 67}{space 3}  .305775
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. *** Appendix D: Policy selection
. /// Multinomial regression models
> /// Model replication of main text (table 1) without the set size variable (footnote XX)
> mlogit pol_select horizon other casualties ///
> gender age party fp_know edu_cat, b(1)

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1024.3835}  
Iteration 1:{space 3}log likelihood = {res:-720.03491}  
Iteration 2:{space 3}log likelihood = {res:-691.27973}  
Iteration 3:{space 3}log likelihood = {res:-690.61312}  
Iteration 4:{space 3}log likelihood = {res:-690.61095}  
Iteration 5:{space 3}log likelihood = {res:-690.61095}  
{res}
{txt}Multinomial logistic regression{col 51}Number of obs{col 67}= {res}      1020
{txt}{col 51}LR chi2({res}48{txt}){col 67}= {res}    667.55
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-690.61095{txt}{col 51}Pseudo R2{col 67}= {res}    0.3258

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  pol_select{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}1           {col 14}{txt}{c |}  (base outcome)
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.4713011{col 26}{space 2}  .200544{col 37}{space 1}   -2.35{col 46}{space 3}0.019{col 54}{space 4}-.8643601{col 67}{space 3}-.0782421
{txt}{space 7}other {c |}{col 14}{res}{space 2} 3.652896{col 26}{space 2} .2140788{col 37}{space 1}   17.06{col 46}{space 3}0.000{col 54}{space 4} 3.233309{col 67}{space 3} 4.072483
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-.8150658{col 26}{space 2} .1969605{col 37}{space 1}   -4.14{col 46}{space 3}0.000{col 54}{space 4}-1.201101{col 67}{space 3}-.4290304
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .2251166{col 26}{space 2} .2059438{col 37}{space 1}    1.09{col 46}{space 3}0.274{col 54}{space 4}-.1785258{col 67}{space 3} .6287589
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0247222{col 26}{space 2} .0081437{col 37}{space 1}   -3.04{col 46}{space 3}0.002{col 54}{space 4}-.0406835{col 67}{space 3}-.0087609
{txt}{space 7}party {c |}{col 14}{res}{space 2} .0113492{col 26}{space 2} .0501859{col 37}{space 1}    0.23{col 46}{space 3}0.821{col 54}{space 4}-.0870134{col 67}{space 3} .1097119
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .2392631{col 26}{space 2} .1281162{col 37}{space 1}    1.87{col 46}{space 3}0.062{col 54}{space 4}-.0118399{col 67}{space 3} .4903662
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .2333884{col 26}{space 2} .1235572{col 37}{space 1}    1.89{col 46}{space 3}0.059{col 54}{space 4}-.0087793{col 67}{space 3} .4755561
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -5.10514{col 26}{space 2} .7364091{col 37}{space 1}   -6.93{col 46}{space 3}0.000{col 54}{space 4}-6.548475{col 67}{space 3}-3.661804
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.8066804{col 26}{space 2} .4025766{col 37}{space 1}   -2.00{col 46}{space 3}0.045{col 54}{space 4}-1.595716{col 67}{space 3}-.0176449
{txt}{space 7}other {c |}{col 14}{res}{space 2} 2.493116{col 26}{space 2} .3815078{col 37}{space 1}    6.53{col 46}{space 3}0.000{col 54}{space 4} 1.745375{col 67}{space 3} 3.240858
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-.5782061{col 26}{space 2} .3671155{col 37}{space 1}   -1.57{col 46}{space 3}0.115{col 54}{space 4}-1.297739{col 67}{space 3} .1413271
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.3925621{col 26}{space 2} .4267621{col 37}{space 1}   -0.92{col 46}{space 3}0.358{col 54}{space 4}   -1.229{col 67}{space 3} .4438763
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0937989{col 26}{space 2} .0257336{col 37}{space 1}   -3.65{col 46}{space 3}0.000{col 54}{space 4}-.1442358{col 67}{space 3} -.043362
{txt}{space 7}party {c |}{col 14}{res}{space 2} .2199768{col 26}{space 2} .1002476{col 37}{space 1}    2.19{col 46}{space 3}0.028{col 54}{space 4} .0234952{col 67}{space 3} .4164585
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .6349966{col 26}{space 2} .2501649{col 37}{space 1}    2.54{col 46}{space 3}0.011{col 54}{space 4} .1446824{col 67}{space 3} 1.125311
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .3002851{col 26}{space 2} .2675331{col 37}{space 1}    1.12{col 46}{space 3}0.262{col 54}{space 4}-.2240702{col 67}{space 3} .8246403
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-5.148551{col 26}{space 2} 1.431195{col 37}{space 1}   -3.60{col 46}{space 3}0.000{col 54}{space 4}-7.953643{col 67}{space 3} -2.34346
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}4            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.9242591{col 26}{space 2} .5600556{col 37}{space 1}   -1.65{col 46}{space 3}0.099{col 54}{space 4}-2.021948{col 67}{space 3} .1734296
{txt}{space 7}other {c |}{col 14}{res}{space 2} 1.030488{col 26}{space 2} .5246696{col 37}{space 1}    1.96{col 46}{space 3}0.050{col 54}{space 4} .0021539{col 67}{space 3} 2.058821
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-.2099238{col 26}{space 2} .4719969{col 37}{space 1}   -0.44{col 46}{space 3}0.656{col 54}{space 4}-1.135021{col 67}{space 3} .7151731
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.3494925{col 26}{space 2} .5472336{col 37}{space 1}   -0.64{col 46}{space 3}0.523{col 54}{space 4}-1.422051{col 67}{space 3} .7230656
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0167065{col 26}{space 2} .0220947{col 37}{space 1}   -0.76{col 46}{space 3}0.450{col 54}{space 4}-.0600113{col 67}{space 3} .0265983
{txt}{space 7}party {c |}{col 14}{res}{space 2}  .137407{col 26}{space 2} .1344181{col 37}{space 1}    1.02{col 46}{space 3}0.307{col 54}{space 4}-.1260476{col 67}{space 3} .4008617
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .1909578{col 26}{space 2} .3294786{col 37}{space 1}    0.58{col 46}{space 3}0.562{col 54}{space 4}-.4548085{col 67}{space 3} .8367241
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2}-.0243851{col 26}{space 2} .3192632{col 37}{space 1}   -0.08{col 46}{space 3}0.939{col 54}{space 4}-.6501294{col 67}{space 3} .6013593
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-4.192804{col 26}{space 2} 1.664228{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-7.454631{col 67}{space 3}-.9309757
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}5            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2}-.0585408{col 26}{space 2} .4903308{col 37}{space 1}   -0.12{col 46}{space 3}0.905{col 54}{space 4}-1.019571{col 67}{space 3} .9024899
{txt}{space 7}other {c |}{col 14}{res}{space 2} 1.236215{col 26}{space 2} .4757681{col 37}{space 1}    2.60{col 46}{space 3}0.009{col 54}{space 4} .3037268{col 67}{space 3} 2.168703
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-.6731631{col 26}{space 2} .4618918{col 37}{space 1}   -1.46{col 46}{space 3}0.145{col 54}{space 4}-1.578455{col 67}{space 3} .2321282
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-1.615933{col 26}{space 2} .6472838{col 37}{space 1}   -2.50{col 46}{space 3}0.013{col 54}{space 4}-2.884586{col 67}{space 3}-.3472801
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0175724{col 26}{space 2} .0207642{col 37}{space 1}   -0.85{col 46}{space 3}0.397{col 54}{space 4}-.0582695{col 67}{space 3} .0231247
{txt}{space 7}party {c |}{col 14}{res}{space 2} .1907413{col 26}{space 2} .1326747{col 37}{space 1}    1.44{col 46}{space 3}0.151{col 54}{space 4}-.0692963{col 67}{space 3} .4507789
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}-.1434075{col 26}{space 2}  .308176{col 37}{space 1}   -0.47{col 46}{space 3}0.642{col 54}{space 4}-.7474213{col 67}{space 3} .4606064
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .0138708{col 26}{space 2} .2912861{col 37}{space 1}    0.05{col 46}{space 3}0.962{col 54}{space 4}-.5570394{col 67}{space 3} .5847811
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-3.239091{col 26}{space 2}  1.49241{col 37}{space 1}   -2.17{col 46}{space 3}0.030{col 54}{space 4}-6.164162{col 67}{space 3}-.3140214
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}6            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2} .5533302{col 26}{space 2} .7104672{col 37}{space 1}    0.78{col 46}{space 3}0.436{col 54}{space 4}-.8391599{col 67}{space 3}  1.94582
{txt}{space 7}other {c |}{col 14}{res}{space 2} 2.132921{col 26}{space 2} .6075957{col 37}{space 1}    3.51{col 46}{space 3}0.000{col 54}{space 4} .9420555{col 67}{space 3} 3.323787
{txt}{space 2}casualties {c |}{col 14}{res}{space 2}-1.403109{col 26}{space 2} .6779295{col 37}{space 1}   -2.07{col 46}{space 3}0.038{col 54}{space 4}-2.731826{col 67}{space 3}-.0743917
{txt}{space 6}gender {c |}{col 14}{res}{space 2} .0780747{col 26}{space 2} .6829963{col 37}{space 1}    0.11{col 46}{space 3}0.909{col 54}{space 4}-1.260574{col 67}{space 3} 1.416723
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0648463{col 26}{space 2} .0365854{col 37}{space 1}   -1.77{col 46}{space 3}0.076{col 54}{space 4}-.1365525{col 67}{space 3} .0068598
{txt}{space 7}party {c |}{col 14}{res}{space 2} .2474518{col 26}{space 2}  .165514{col 37}{space 1}    1.50{col 46}{space 3}0.135{col 54}{space 4}-.0769497{col 67}{space 3} .5718534
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2}  1.01374{col 26}{space 2} .4291033{col 37}{space 1}    2.36{col 46}{space 3}0.018{col 54}{space 4} .1727133{col 67}{space 3} 1.854767
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .0238536{col 26}{space 2} .4441119{col 37}{space 1}    0.05{col 46}{space 3}0.957{col 54}{space 4}-.8465898{col 67}{space 3}  .894297
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-6.802534{col 26}{space 2} 2.365875{col 37}{space 1}   -2.88{col 46}{space 3}0.004{col 54}{space 4}-11.43956{col 67}{space 3}-2.165505
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}7            {txt}{c |}
{space 5}horizon {c |}{col 14}{res}{space 2} .3070466{col 26}{space 2} .6397279{col 37}{space 1}    0.48{col 46}{space 3}0.631{col 54}{space 4} -.946797{col 67}{space 3}  1.56089
{txt}{space 7}other {c |}{col 14}{res}{space 2} 1.167624{col 26}{space 2} .6090858{col 37}{space 1}    1.92{col 46}{space 3}0.055{col 54}{space 4}-.0261621{col 67}{space 3}  2.36141
{txt}{space 2}casualties {c |}{col 14}{res}{space 2} -.794138{col 26}{space 2} .5944714{col 37}{space 1}   -1.34{col 46}{space 3}0.182{col 54}{space 4}-1.959281{col 67}{space 3} .3710046
{txt}{space 6}gender {c |}{col 14}{res}{space 2}-.0639726{col 26}{space 2} .6280591{col 37}{space 1}   -0.10{col 46}{space 3}0.919{col 54}{space 4}-1.294946{col 67}{space 3} 1.167001
{txt}{space 9}age {c |}{col 14}{res}{space 2}-.0181409{col 26}{space 2} .0263846{col 37}{space 1}   -0.69{col 46}{space 3}0.492{col 54}{space 4}-.0698538{col 67}{space 3} .0335719
{txt}{space 7}party {c |}{col 14}{res}{space 2} .2772957{col 26}{space 2} .1652516{col 37}{space 1}    1.68{col 46}{space 3}0.093{col 54}{space 4}-.0465914{col 67}{space 3} .6011828
{txt}{space 5}fp_know {c |}{col 14}{res}{space 2} .1321452{col 26}{space 2} .3836126{col 37}{space 1}    0.34{col 46}{space 3}0.730{col 54}{space 4}-.6197216{col 67}{space 3}  .884012
{txt}{space 5}edu_cat {c |}{col 14}{res}{space 2} .1137334{col 26}{space 2} .3782081{col 37}{space 1}    0.30{col 46}{space 3}0.764{col 54}{space 4}-.6275409{col 67}{space 3} .8550076
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} -5.62859{col 26}{space 2} 2.016361{col 37}{space 1}   -2.79{col 46}{space 3}0.005{col 54}{space 4}-9.580585{col 67}{space 3}-1.676595
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. *** Appendix E: Contextual preference reversal
. /// Cross-tabs of selected policy
> /// Including number and precentage of selection
> /// Reporting chi-square test for differences
> tab seta_2 seta_3, row chi2
{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

           {c |}              SetA_3
    SetA_2 {c |}         0          1          2 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}       332         86         34 {txt}{c |}{res}       452 
           {txt}{c |}{res}     73.45      19.03       7.52 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
         1 {c |}{res}        33        461         90 {txt}{c |}{res}       584 
           {txt}{c |}{res}      5.65      78.94      15.41 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}       365        547        124 {txt}{c |}{res}     1,036 
           {txt}{c |}{res}     35.23      52.80      11.97 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}518.9142  {txt} Pr = {res}0.000
{txt}
{com}. tab setb_2 setb_3, row chi2
{txt}
{c TLC}{hline 16}{c TRC}
{c |} Key{col 18}{c |}
{c LT}{hline 16}{c RT}
{c |}{space 3}{it:frequency}{col 18}{c |}
{c |}{space 1}{it:row percentage}{col 18}{c |}
{c BLC}{hline 16}{c BRC}

           {c |}              SetB_3
    SetB_2 {c |}         0          1          2 {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
         0 {c |}{res}       459         29        477 {txt}{c |}{res}       965 
           {txt}{c |}{res}     47.56       3.01      49.43 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
         1 {c |}{res}        21         40         10 {txt}{c |}{res}        71 
           {txt}{c |}{res}     29.58      56.34      14.08 {txt}{c |}{res}    100.00 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}       480         69        487 {txt}{c |}{res}     1,036 
           {txt}{c |}{res}     46.33       6.66      47.01 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}304.6344  {txt} Pr = {res}0.000
{txt}
{com}. 
{txt}end of do-file

{com}. do "/var/folders/_k/f5hpyr457tsfw0m8rxfzlhfh0000gn/T//SD91882.000000"
{txt}
{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/admin/Dropbox/TAMU/Diss./Theory/Choice_set/RnR/Dataverse/FPA_Appx.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 1 Jan 2021, 23:48:42
{txt}{.-}
{smcl}
{txt}{sf}{ul off}